fundamentals
Get fundamental financial data including financials, earnings, and key metrics. Use when user asks about financials, earnings, revenue, profit, balance sheet, income statement, or company fundamentals.
pinned to #cc30858updated 2 weeks ago
Ask your AI client: “install skills/fundamentals”.
Requires the metahub MCP server installed in your client. Set up MCP.
mh install skills/fundamentalsmetahub onboarded this repo on the author's behalf.
If you own github.com/staskh/trading_skills on GitHub, claim the listing to take over publishing. Your claim preserves the existing eval history and badges; only the curator label is replaced with verified-publisher on your next publish.
Stars
288
Last commit
2 weeks ago
Latest release
published
- #ai-trading
- #claude
- #claude-skills
- #mcp-server
- #option-trading
- #options-trading
About this skill
Pulled from SKILL.md at publish time.
Fetch fundamental financial data from Yahoo Finance.
Evaluation report
WarningsAutomated checks the publisher passed at publish time — structure, docs, safety, and whether the artifact behaves as claimed.cc30858· 2 weeks ago
Kind-specific
31Skill: SKILL.md present
found at .claude/skills/fundamentals/SKILL.md · frontmatter source: SKILL.md
Skill: body content present
447 words · 3,065 chars · 13 sections · 2 code blocks
Skill: triggers declaredwarn
No `trigger` phrases in SKILL.md frontmatter
Add `trigger:` lines so Claude knows when to activate this skill — e.g. `when building MCP servers` or `for diagram creation`.
Skill: allowed-tools scope
no allowed-tools restriction (Claude may use anything)
Release history
1- releasecurrentcc30858warn2 weeks ago
Contents
Fetch fundamental financial data from Yahoo Finance.
Instructions
Note: If
uvis not installed orpyproject.tomlis not found, replaceuv run pythonwithpythonin all commands below.
uv run python scripts/fundamentals.py SYMBOL [--type TYPE]
Arguments
SYMBOL- Ticker symbol--type- Data type: all, financials, earnings, info (default: all)
Output
Returns JSON with:
info- Key metrics (market cap, PE, EPS, dividend, etc.)financials- Recent quarterly/annual income statement dataearnings- Historical and estimated earnings
Present key metrics clearly. Compare actual vs estimated earnings if relevant.
Piotroski F-Score
Calculate Piotroski's F-Score to evaluate a company's financial strength using 9 fundamental criteria.
Instructions
uv run python scripts/piotroski.py SYMBOL
What is Piotroski F-Score?
Piotroski's F-Score is a fundamental analysis tool developed by Joseph Piotroski that evaluates a company's financial strength using 9 criteria. Each criterion scores 1 point if passed, 0 if failed, for a maximum score of 9.
The 9 Criteria
- Positive Net Income - Company is profitable
- Positive ROA - Assets are generating returns
- Positive Operating Cash Flow - Company generates cash from operations
- Cash Flow > Net Income - High-quality earnings (cash exceeds accounting profit)
- Lower Long-Term Debt - Decreasing leverage (improving financial position)
- Higher Current Ratio - Improving liquidity
- No New Shares Issued - No dilution (or share buybacks)
- Higher Gross Margin - Improving profitability efficiency
- Higher Asset Turnover - More efficient use of assets
Score Interpretation
- 8-9: Excellent - Very strong financial health
- 6-7: Good - Strong financial health
- 4-5: Fair - Moderate financial health
- 0-3: Poor - Weak financial health
Output
Returns JSON with:
score- F-Score (0-9)max_score- Maximum possible score (9)criteria- Detailed breakdown of each criterion with pass/fail status and valuesinterpretation- Text description of financial health leveldata_available- Boolean indicating if year-over-year comparison data is available for criteria 5-9
Implementation Details
- Criteria 1-4 use quarterly financial data (most recent year)
- Criteria 5-9 use annual financial data for year-over-year comparisons
- Compares most recent fiscal year vs previous fiscal year
Use Cases
Use Piotroski F-Score when:
- Evaluating fundamental financial strength
- Screening for value stocks with improving fundamentals
- Assessing financial health trends
- Comparing financial strength across companies
- Identifying companies with strong fundamentals but undervalued prices
Dependencies
pandasyfinance
Timezone
All timestamps and time-based calculations must use the America/New_York timezone. All JSON output must include generated_at (NY time string) and data_delay fields.
Reviews
No reviews yet. Be the first.
Related
Planning With Files
Claude Code skill implementing Manus-style persistent markdown planning — the workflow pattern behind the $2B acquisition.
Frontend Slides
Create beautiful slides on the web using Claude's frontend skills
Gpt Researcher
An autonomous agent that conducts deep research on any data using any LLM providers
mh install skills/fundamentals